Performance and evaluation of energy optimization techniques for wireless body area networks

Abstract

Background

Wireless body area networks are created to retrieve and transmit human health information by using sensors on the human body. Energy efficiency is considered a foremost challenge to increase the lifetime of a network. To deal with energy efficiency, one of the important mechanisms is selecting the relay node, which can be modeled as an optimization problem. These days nature-inspired algorithms are being widely used to solve various optimization problems. With regard to this, this paper aims to compare the performance of the three most recent nature-inspired metaheuristic algorithms for solving the relay node selection problem.

Results

It has been found that the total energy consumption calculated using grey wolf optimization decreased by 23% as compared to particle swarm optimization and 16% compared to ant lion optimization.

Conclusions

The results suggest that grey wolf optimization is better than the other two techniques due to its social hierarchy and hunting behavior. The findings showed that, compared to well-known heuristics such as particle swarm optimization and ant lion optimization, grey wolf optimization was able to deliver extremely competitive results.